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Sora for Computational Social systems: From Counterfactual Experiments to Artificiofactual Experiments With Parallel Intelligence

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第2期11卷 1531-1550页

作者： Qin, Rui Wang, Fei-Yue Zheng, Xiaolong Ni, Qinghua Li, Juanjuan Xue, Xiao Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Software Tianjin Peoples R China Beijing Inst Technol Sch Med Technol Beijing Peoples R China

Welcome to the second issue of IEEE Transactions on Computational Social systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.

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Event-triggered optimal control for discrete-time multi-player non-zero-sum games using parallel control

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INFORMATION SCIENCES 2022年 584卷 519-535页

作者： Lu, Jingwei Wei, Qinglai Wang, Ziyang Zhou, Tianmin Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This paper presents a novel event-triggered optimal control (ETOC) method for discrete -time (DT) multi-player non-zero-sum games (NZSGs). First, a novel event-triggered algo-rithm is developed for DT multi-player NZSGs based on the time-triggered optimal value functions. Therefore, the developed event-triggered algorithm only needs to solve the time-triggered Hamilton-Jacobi-Bellman (HJB) equations. Then, the asymptotic stability of the closed-loop system is proved. Additionally, we show that an upper bound for the sum of the actual performance indices of all the players can be determined in advance. A key step in the implementation of the developed event-triggered algorithm is to obtain the next state of the actual system, which is difficult to implement on the actual system. Thus, a parallel control method is utilized to predict the next state by constructing the par-allel system for the actual system and obtain the optimal value functions. The method that combines the developed event-triggered algorithm and parallel control is called the event-triggered optimal parallel control (ETOPC) method. The neural network (NN) technique and the iterative adaptive dynamic programming (IADP) technique are employed in parallel control. Moreover, the control stability is shown further in the consideration of the NN weight approximation errors. Finally, two simulations justify the theoretical conjectures. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Event-triggered Non-zero-sum games Parallel control Neural network Adaptive dynamic programming

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Unconstrained end-to-end text reading with feature rectification

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PATTERN RECOGNITION LETTERS 2021年 149卷 1-8页

作者： Du, Chen Wang, Yanna Wang, Chunheng Xiao, Baihua Shi, Cunzhao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

We propose an end-to-end trainable network that can simultaneously localize and recognize irregular text from images. Specifically, we find the feature incompatibility problem, which arises from the contradiction between detection and recognition tasks for feature extraction of the convolutional neural network, and propose to introduce the larger-scale features for the recognition part to improve the accuracy of recognition instead of using the same feature with the detection. To extract effective text features for perspective and curved text recognition, we propose a position-sensitive network to rectify the text proposal features in the recognition branch. The position-sensitive network, which is trained in a weak supervision way, takes the proposal detection feature as input and outputs the feature rectification information. Experiments demonstrate that the proposed method can achieve state-of-the-art or highly competitive performance compared with baselines on a number of benchmarks. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Text recognition Text detection Position-sensitive network Features incompatibility End-to-end

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Region-Farm Crop Planning Through Double Deep Q-Learning Toward Sustainable Agriculture

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7608-7617页

作者： Wang, Xiujuan Xu, Yulin Wang, Haoyu Kang, Mengzhen Hua, Jing Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Global food market faces escalating risks and uncertainties, bringing great challenges in balancing a country's food supply and demand. Therefore, it is of great significance to carry out crop planning and reasonably divide the planting area of each crop to ensure the national food security. However, the existing planting planning methods have the problems of inaccurate crop price prediction and poor flexibility, and challenges remain on how to motivate farmers. With the rapid development of science and technology, agricultural crop planning techniques have made great progress. This study focuses on agricultural planting planning, exploring both planting area planning based on predicted crop prices and a crop allocation model within a multifarmer context. The regional planting goals are decomposed into specific allocations for individual farmers and plots, addressing objectives including maximizing farmer profits and expanding soybean cultivation for national self-sufficiency. The work employs the long short-term memory (LSTM) model to predict the prices of soybean, wheat, and maize. First, linear programming model is applied to plan planting areas of crops, incorporating constraints to encourage sustainable agricultural practices. Second, a multifarmer crop allocation model, utilizing the double deep Q network (DDQN) algorithm, is developed to enhance the fairness among farmers and assure rotational benefits. Experimental validation confirms the effectiveness of the proposed algorithms, providing valuable decision support for agricultural planning with economic and ecological sustainability.

关键词： Crops Planning Biological system modeling Resource management Predictive models Linear programming Fertilizers Crop rotation multifarmer crop allocation planting area planning planting plan price prediction reinforcement learning staple food

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Cross-Domain Person Re-Identification Using Heterogeneous Convolutional Network

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2022年第3期32卷 1160-1171页

作者： Zhang, Zhong Wang, Yanan Liu, Shuang Xiao, Baihua Durrani, Tariq S. Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland

Person re-identification (Re-ID) is a challenging task due to variations in pedestrian images, especially in cross-domain scenarios. The existing cross-domain person Re-ID approaches extract the feature from single pedestrian image, but they ignore the correlations among pedestrian images. In this paper, we propose Heterogeneous Convolutional Network (HCN) for cross-domain person Re-ID, which learns the appearance information of pedestrian images and the correlations among pedestrian images simultaneously. To this end, we first utilize Convolutional Neural Network (CNN) to extract the appearance features for pedestrian images. Then we construct a graph in the target dataset where the appearance features are treated as the nodes and the similarity represents the linkage between the nodes. Afterwards, we propose Dual Graph Convolution (DGConv) to explicitly learn the correlation information from the similar and dissimilar samples, which could avoid the over-smoothing caused by the fully connected graph. Furthermore, we design HCN as a multi-branch structure to mine the structural information of pedestrians. We conduct extensive evaluations for HCN on three datasets, i.e. Market-1501, DukeMTMC-reID and MSMT17, and the results demonstrate that HCN is superior to the state-of-the-art methods.

关键词： Correlation Feature extraction Couplings Convolution Training Cameras Loss measurement Cross-domain person re-identification graph convolution network dual graph convolution

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Learning to Assemble Noncylindrical Parts Using Trajectory Learning and Force Tracking

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 3132-3143页

作者： Su, Jianhua Meng, Yan Wang, Lili Yang, Xu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Chinese Acad Sci Beijing 100864 Peoples R China

The purpose of this article is to teach a robot assembly skills from demonstrations, and we attempt to train both the trajectory and the insertion force simultaneously. We encode human demonstrations data via motion primitives and, then, generate a reference trajectory and a prescribed force profile for a new assembly task using the combination of the motion primitives. We then propose an adaptive impedance controller to track the prescribed force with unknown environment stiffness, where the impedance parameters are estimated by the optimal solution of an equivalent linearization model. Our approach combines adaptive impedance control techniques with learning from demonstration on the same that makes it tractable and applicable to a real robot. Experiments on several typical noncylindrical parts illustrate the efficiency of the proposed method.

关键词： Assembly skill impedance control learning from demonstration movement primitives (MPs)

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GLaLT: Global-Local Attention-Augmented Light Transformer for Scene Text Recognition

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第7期35卷 10145-10158页

作者： Zhang, Hui Luo, Guiyang Kang, Jian Huang, Shan Wang, Xiao Wang, Fei-Yue Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China China Telecom Beijing 100033 Peoples R China Tencent Dept Informat Secur Beijing 100193 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230093 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Recent years have witnessed the growing popularity of connectionist temporal classification (CTC) and attention mechanism in scene text recognition (STR). CTC-based methods consume less time with few computational burdens, while they are not as effective as attention-based methods. To retain computational efficiency and effectiveness, we propose the global-local attention-augmented light Transformer (GLaLT), which adopts a Transformer-based encoder-decoder structure to orchestrate CTC and attention mechanism. The encoder integrates the self-attention module with the convolution module to augment the attention, where the self-attention module pays more attention to capturing long-term global dependencies and the convolution module focuses on local context modeling. The decoder consists of two parallel modules: one is the Transformer-decoder-based attention module and the other is the CTC module. The first one is removed in the testing phase and can guide the second one to extract robust features in the training phase. Extensive experiments on standard benchmarks demonstrate that GLaLT achieves state-of-the-art performance for both regular and irregular STR. In terms of tradeoffs, the proposed GLaLT is at or near the frontiers for maximizing speed, accuracy, and computational efficiency at the same time.

关键词： Text recognition Transformers Decoding Character recognition Feature extraction Image recognition Training Attention mechanism connectionist temporal classification (CTC) scene text recognition (STR)

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Social relation and physical lane aggregator:integrating social and physical features for multimodal motion prediction

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Journal of Intelligent and Connected Vehicles 2022年第3期5卷 302-308页

作者： Qiyuan Chen Zebing Wei Xiao Wang Lingxi Li Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIndianaUSA Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina

Purpose–The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction,which is critical for autonomous *** is obvious that traffic agents’trajectories are influenced by physical lane rules and agents’social ***/methodology/approach–In this paper,the authors propose the social relation and physical lane aggregator for multimodal motion prediction,where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention ***–The proposed methods are evaluated on the Waymo Open Motion Dataset,and the results show the effectiveness of the proposed two feature aggregation modules for trajectory ***/value–This paper proposes a new design method to extract traffic interactions,and the attention mechanism is used in each part of the model to extract and fuse different relational features,which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.

关键词： Deep learning Machine learning Autonomous driving Trajectory prediction

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DSNRNet: A Network of LiDAR Data Denoising by Differential Stability in Underground Mine 26

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Meng, Dejiang Tian, Bin Pan, Ziyu Wang, Junhui Chen, Long Waytous Inc Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9798350399462

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8%) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2%) and WeatherNet (436.8ms, 98.5%).

关键词： Object recognition

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AR-C&P: A Tangible Programming for Children Based Augmented Reality 22

AR-C&P: A Tangible Programming for Children Based Augmented ...

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Proceedings of the Tenth International Symposium of Chinese CHI

作者： Danli Wang Yanyan Zhao Shuo Feng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450398695

Augmented Reality (AR) technology enables continuous visual feedback and varied digital scenes on the real world. This paper presents AR-C&P, an AR-based tangible programming tool for young children. It allows children to create AR maze map with low-cost materials and plan the route by constructing tangible program sequences. Meanwhile, a variety of real-time visual feedback from the screen will be superimposed on the real scenes of the whole process. AR-C&P is intuitive and intriguing which could enrich children’s creating experience, develops their cognitive developmental skills and helps children learn fundamental programming concepts. We conducted two experiments. The first experiment reveals the relationship between task completion and cognitive developmental skills. The second experiment reveals the effect of AR on learning programming.

关键词： Augmented Reality children edutainment programming languages tangible programming

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